Skip to main content
SpringerLink
Log in

Automated classification of emphysema using data augmentation and effective pixel location estimation with multi-scale residual network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In modern medical diagnosis also, the emphysema is still recognized by the computed tomography (CT) scans with a set of defined patterns as a classification problem in computer vision. There were as many algorithms developed in the past that attempt to classify the underlying patterns and their relevant associated clusters by modeling an automated system. And this classification modeling approach is responsible for the benchmarking classification and quantification of various emphysematous tissues from lung CT images on different scales in the literature. Hence, with the same motivation and intents, this article put forth a multiscale residual network with data augmentation model (MS-ResNet-DA). First, a generative adversarial network (GAN) is employed to augment the training samples and avoid the overfitting problem. These images are again augmented based on different image processing methods. Then, the obtained images are learned by MS-ResNet to categorize the emphysema. Still, the accuracies of categorizing the centrilobular emphysema (CLE) and panlobular emphysema (PLE) are not satisfactory because they do not have spatial dependence. So, an enhanced MS-ResNet-DA (EMS-ResNet-DA) model is proposed, which applies an effective position estimation algorithm to measure relative and absolute location data of emphysema pixels in the images. The relative location data give the current location of the emphysema pixel by extracting the relative dislocation measures from CT images. Also, the absolute location estimation model is based on the position encoding network to match the diseased image with the reference emphysema images and validate whether location data are implicitly learned when trained on categorical labels. Moreover, these location data of all pixels in the images are learned by the MS-ResNet for emphysema classification. Finally, the experimental results demonstrated that the EMS-ResNet-DA achieves an overall classification accuracy of 94.6% that outclasses the conventional models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Salvi S, Kumar GA, Dhaliwal RS, Paulson K, Agrawal A, Koul PA, Christopher DJ (2018) The burden of chronic respiratory diseases and their heterogeneity across the states of India: the global burden of disease study 1990–2016. Lancet Glob Health 6(12):1363–1374

    Article  Google Scholar 

  2. Hochhegger B, Langer FW, Irion K, Souza A, Moreira J, Baldisserotto M, Pallaoro Y, Muller E, Medeiros TM, Altmayer S, Marchiori E (2019) Pulmonary acinus: understanding the computed tomography findings from an acinar perspective. Lung 197(3):259–265

    Article  Google Scholar 

  3. Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, Coxson HO (2015) CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner society. Radiology 277(1):192–205

    Article  Google Scholar 

  4. Lynch DA, Moore CM, Wilson C, Nevrekar D, Jennermann T, Humphries SM, Regan EA (2018) CT-based visual classification of emphysema: association with mortality in the COPDGene study. Radiology 288(3):859–866

    Article  Google Scholar 

  5. Stockley RA, Halpin DM, Celli BR, Singh D (2019) Chronic obstructive pulmonary disease biomarkers and their interpretation. Am J Respir Crit Care Med 199(10):1195–1204

    Article  Google Scholar 

  6. Yang J, Angelini ED, Smith BM, Austin JH, Hoffman EA, Bluemke DA, Laine AF (2016) Explaining radiological emphysema subtypes with unsupervised texture prototypes: MESA COPD study. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. Springer, Cham, pp 69–80

    Google Scholar 

  7. Binder P, Batmanghelich NK, Estepar RSJ, Golland P (2016) Unsupervised discovery of emphysema subtypes in a large clinical cohort. International Workshop on Machine Learning in Medical Imaging. Springer, Cham, pp 180–187

    Chapter  Google Scholar 

  8. Peng L, Lin L, Hu H, Ling X, Wang D, Han X, Chen YW (2017) Joint weber-based rotation invariant uniform local ternary pattern for classification of pulmonary emphysema in CT images. In: IEEE international conference on image processing, pp 2050–2054

  9. Sorensen L, Shaker SB, De Bruijne M (2010) Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans Med Imaging 29(2):559–569

    Article  Google Scholar 

  10. Mendoza CS, Washko GR, Ross JC, Diaz AA, Lynch DA, Crapo JD, Estépar RSJ (2012) Emphysema quantification in a multi-scanner HRCT cohort using local intensity distributions. In 9th IEEE international symposium on biomedical imaging, pp 474–477

  11. Yang J, Feng X, Angelini ED, Laine AF (2016) Texton and sparse representation based texture classification of lung parenchyma in CT images. In: 38th Annual international conference of the IEEE engineering in medicine and biology society, pp 1276–1279

  12. Xu Y, van Beek EJ, Hwanjo Y, Guo J, McLennan G, Hoffman EA (2006) Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). Acad Radiol 13(8):969–978

    Article  Google Scholar 

  13. Vasconcelos V, Barroso J, Marques L, Silvestre Silva J (2015) Enhanced classification of interstitial lung disease patterns in HRCT images using differential lacunarity. Biomed Res Int 2015:1–9

    Article  Google Scholar 

  14. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216

    Article  Google Scholar 

  15. Cheplygina V, Pena IP, Pedersen JH, Lynch DA, Sørensen L, de Bruijne M (2017) Transfer learning for multicenter classification of chronic obstructive pulmonary disease. IEEE J Biomed Health Inform 22(5):1486–1496

    Article  Google Scholar 

  16. Peng L, Lin L, Hu H, Li H, Chen Q, Ling X, Chen YW (2019) Classification and quantification of emphysema using a multi-scale residual network. IEEE J Biomed Health Inform 23(6):2526–2536

    Article  Google Scholar 

  17. Gao M, Bagci U, Lu L, Wu A, Buty M, Shin HC, Xu Z (2018) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6(1):1–6

    Article  Google Scholar 

  18. Humphries SM, Notary AM, Centeno JP, Lynch DA (2018) Automatic classification of centrilobular emphysema on CT using deep learning: comparison with visual scoring. Image analysis for moving organ, breast, and thoracic images. Springer, Cham, pp 319–325

    Chapter  Google Scholar 

  19. Pino Peña I, Cheplygina V, Paschaloudi S, Vuust M, Carl J, Weinreich UM, de Bruijne M (2018) Automatic emphysema detection using weakly labeled HRCT lung images. PLoS ONE 13(10):1–16

    Article  Google Scholar 

  20. Ge Y, Wang Q, Wang L, Wu H, Peng C, Wang J, Yi Y (2019) Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inform 132:1–32

    Article  Google Scholar 

  21. Gupta N, Gupta D, Khanna A, RebouçasFilho PP, de Albuquerque VHC (2019) Evolutionary algorithms for automatic lung disease detection. Measurement 140:590–608

    Article  Google Scholar 

  22. Jimenez-Carretero D, Bermejo-Peláez D, Nardelli P, Fraga P, Fraile E, Estépar RSJ, Ledesma-Carbayo MJ (2019) A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. Med Image Anal 52:144–159

    Article  Google Scholar 

  23. Joyseeree R, Otálora S, Müller H, Depeursinge A (2019) Fusing learned representations from Riesz filters and deep CNN for lung tissue classification. Med Image Anal 56:172–183

    Article  Google Scholar 

  24. Xu M, Qi S, Yue Y, Teng Y, Xu L, Yao Y, Qian W (2019) Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. Biomed Eng Online 18(1):1–21

    Article  Google Scholar 

  25. Surendar P, Ponni M (2021) Diagnosis of lung cancer using hybrid deep neural network with adaptive sine cosine crow search algorithm. J Comput Sci 53:101374. https://doi.org/10.1016/j.jocs.2021.101374

    Article  MathSciNet  Google Scholar 

  26. Tang C, Plasek JM, Zhang H, Kang MJ, Sheng H, Xiong Y, Zhou L (2019) A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes. BMC Med Inform Decis Mak 19(8):1–9

    Google Scholar 

  27. Liu H, Wang L, Nan Y, Jin F, Wang Q, Pu J (2019) SDFN: segmentation-based deep fusion network for thoracic disease classification in chest X-ray images. Comput Med Imaging Graph 75:66–73

    Article  Google Scholar 

  28. Balamurugan D, Aravinth SS, Reddy PCS et al (2022) Multiview objects recognition using deep learning-based Wrap-CNN with voting scheme. Neural Process Lett 54:1495–1521. https://doi.org/10.1007/s11063-021-10679-4

    Article  Google Scholar 

  29. Cazzolato MT, Scabora LC, Nesso-Jr MR, Milano-Oliveira LF, Costa AF, Kaster DS, Traina AJ (2019) dp-breath: Heat maps and probabilistic classification assisting the analysis of abnormal lung regions. Comput Methods Programs Biomed 173:27–34

    Article  Google Scholar 

  30. Ke Q, Zhang J, Wei W, Połap D, Woźniak M, Kośmider L, Damaševĭcius R (2019) A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Syst Appl 126:218–232

    Article  Google Scholar 

  31. Gerard SE, Herrmann J, Kaczka DW, Musch G, Fernandez-Bustamante A, Reinhardt JM (2020) Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species. Med Image Anal 60:1–41

    Article  Google Scholar 

  32. Annamalai M, Muthiah P (2022) An early prediction of tumor in heart by cardiac masses classification in echocardiogram images using robust back propagation neural network classifier. Braz Arch Biol Technol. https://doi.org/10.1590/1678-4324-2022210316

    Article  Google Scholar 

  33. Nagaraj Y, Wisselink HJ, Rook M, Cai J, Nagaraj SB, Sidorenkov G, Veldhuis R, Oudkerk M, Vliegenthart R, van Ooijen P (2022) AI-driven model for automatic emphysema detection in low-dose computed tomography using disease-specific augmentation. J Digit Imaging 35:1–13

    Article  Google Scholar 

  34. Lor KL, Chang YC, Yu CJ, Wang CY, Chen CM (2022) Bullous parametric response map for functional localization of COPD. J Digit Imaging 35:1–12

    Article  Google Scholar 

  35. https://lauge-soerensen.github.io/emphysema-database/

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, Thiruchengode, Tamilnadu, India

    T. Manikandan

  2. Department of Electrical and Electronics Engineering, Kongu Engineering College, Erode, Tamilnadu, India

    S. Maheswari

Authors
  1. T. Manikandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Maheswari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Manikandan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manikandan, T., Maheswari, S. Automated classification of emphysema using data augmentation and effective pixel location estimation with multi-scale residual network. Neural Comput & Applic 34, 20899–20914 (2022). https://doi.org/10.1007/s00521-022-07566-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07566-x

Keywords

Search

Navigation